“She was a little bit unrealistic”: Choice in healthcare decision making for older people

This paper examines autonomy, choice, options, and power in healthcare decision making for older people. Using discourse analysis and a case study from data gathered as part of an ethnographic field study we critique a common conceptualization of healthcare decision making as patients choosing from an array of options offered by healthcare providers. A discourse of “giving options and being realistic” used by healthcare providers is contrasted with the experience of a single patient's transitional care from hospital to home after hip fracture. This illustrates how a wide variety of actors, institutions, values, and resources take precedence in determining a discharge destination. While the accounts given by healthcare providers cast patient choice in respectful terms, an ethnographic approach illustrates that the “choices” are structured by a discourse which simplifies the complexity of what is offered and who gets to choose. In the case study the patient's choice was subjugated by expertise and institutional concerns; her options were largely illusory; and her autonomy was “at risk” due to her age, poor health, and limited resources. We use Foucault's ideas about discourse and governmentality to question the scope of agency in healthcare decision making. We argue that the conceptualization of informed patients making autonomous choices acts as “misdirection” which deflects problem solving and discussion away from a productive examination of the differences between healthcare system offerings and client needs. We conclude by posing questions to reorient the debate surrounding healthcare decision making for older adults and recommend a more participatory approach to designing social services.     

When exposure is subject to nondifferential misclassification,are validation data helpful in testing for an exposure–disease association?

Consider assessing the evidence for an exposure variable and a disease variable being associated, when the true exposure variable is more costly to obtain than an error‐prone but nondifferential surrogate exposure variable. From a study design perspective, there are choices regarding the best use of limited resources. Should one acquire the true exposure status for fewer subjects or the surrogate exposure status for more subjects? The issue of validation is also central, i.e., should we simultaneously measure the true and surrogate exposure variables on a subset of study subjects? Using large‐sample theory, we provide a framework for quantifying the power of testing for an exposure–disease association as a function of study cost. This enables us to present comparisons of different study designs under different suppositions about both the relative cost and the performance (sensitivity and specificity) of the surrogate variable. We present simulations to show the applicability of our theoretical framework, and we provide a case‐study comparing results from an actual study to what could have been seen had true exposure status been ascertained for a different proportion of study subjects. We also describe an extension of our ideas to a more complex situation involving covariates. The Canadian Journal of Statistics 47: 222–237; 2019 © 2019 Statistical Society of Canada     

Inequality of opportunity and (unequal) opportunities in the youth labour market: How is the Arab world different?

The Middle East and North Africa (MENA) labour market is emblematic of widespread youth unemployment and high discouragement rates, experiencing disproportionately high levels of unemployment among educated young people. Using ILO School-to-Work Transition Surveys for Egypt, Jordan and Tunisia, the authors explore whether this is related to inequality of opportunity or to deeper structural characteristics that create a mismatch between skill demand and supply on the labour market. The low availability of high-skilled jobs and the low value placed on skills gained through the system of vocational training are found to have high explanatory power.     

Multivariate association test for rare variants controlling for cryptic and family relatedness

In genetic studies of complex diseases, multiple measures of related phenotypes are often collected. Jointly analyzing these phenotypes may improve statistical power to detect sets of rare variants affecting multiple traits. In this work, we consider association testing between a set of rare variants and multiple phenotypes in family‐based designs. We use a mixed linear model to express the correlations among the phenotypes and between related individuals. Given the many sources of correlations in this situation, deriving an appropriate test statistic is not straightforward. We derive a vector of score statistics, whose joint distribution is approximated using a copula. This allows us to have closed‐form expressions for the p‐values of several test statistics. A comprehensive simulation study and an application to Genetic Analysis Workshop 18 data highlight the gains associated with joint testing over univariate approaches, especially in the presence of pleiotropy or highly correlated phenotypes. The Canadian Journal of Statistics 47: 90–107; 2019 © 2018 Statistical Society of Canada     

A comparison of algorithms for exact goodness-of-fit tests for multinomial data

Multinomial goodness-of-fit tests arise in a diversity of milieu. The long history of the problem has spawned a multitude of asymptotic tests. If the sample size relative to the number of categories is small, the accuracy of these tests is compromised. In that case, an exact test is a prudent option. But such tests are computationally intensive and need efficient algorithms. This paper gives a conceptual overview, and empirical comparisons of two avenues, namely the network and fast Fourier transform (FFT) algorithms, for an exact goodness-of-fit test on a multinomial. We show that a recursive execution of a polynomial product forms the basis of both these approaches. Specific details to implement the network method, and techniques to enhance the efficiency of the FFT algorithm are given. Our empirical comparisons show that for exact analysis with the chi-square and likelihood ratio statistics, the network-cum-polynomial multiplication algorithm is the more efficient and accurate of the two.     

Nonparametric Estimation of Average Growth Curve with General Nonstationary Error Process

The nonparametric estimation of the growth curve has been extensively studied in both stationary and some nonstationary particular situations. In this work, we consider the statistical problem of estimating the average growth curve for a fixed design model with nonstationary error process. The nonstationarity considered here is of a general form, and this article may be considered as an extension of previous results. The optimal bandwidth is shown to depend on the singularity of the autocovariance function of the error process along the diagonal. A Monte Carlo study is conducted in order to assess the influence of the number of subjects and the number of observations per subject on the estimation.     

On the application of statistical learning approaches to construct inverse probability weights in marginal structural Cox models: Hedging against weight-model misspecification

The marginal structural Cox model (MSCM) estimates can be highly sensitive to weight-model misspecification. We assess the performance of various popular statistical learners, such as LASSO, support vector machines, CART, bagged CART, and boosted CART, in estimating MSCM weights. When weight-models are misspecified, we find that the weights computed from boosted CART generally lead to less MSE and better coverage for the MSCM estimates. This study is motivated by the investigation of the impact of beta-interferon treatment on disability progression in subjects with multiple sclerosis from British Columbia, Canada (1995–2008).     

Critical realist encounters: Morphogenizing the French régulation approach

Throughout the past two decades, Bob Jessop has drawn considerable attention to the compatibility between French Régulation (FR) and Critical Realist approaches (CR), arguing that FR implicitly works within a critical realist ontology, epistemology and methodology. Inspired by his insights, I argue that a Spinozian-led Immanent Causality Morphogenetic Approach (ICMA) provides a fruitful avenue for further regulationist research and represents a promising effort to ground FR in (meta)theory, whereby, ontologically speaking, the ICMA explores how structure and agency emerge, intertwine and redefine each other in and over time. The two approaches mutually reinforce each other: ICMA is able to provide FR with a solid theoretical and metatheoretical foundation, while FR, can enrich the ICMA with its direct engagement with capitalism related studies and a well-developed terminology in the field. The value added of ICMA can be seen in four points: it (1) clarifies the distinction between extensive and intensive regimes of accumulation and the speed of technological change, (2) specifies the problematique of hierarchy and stability of the dominant bloc, (3) fleshes out the problematique of endometabolism and hybridity, and (4) provides the researcher with a methodological framework to absent the necessary relations at the level of conditioning.     

Optimal and Robust Designs for Estimating the Concentration Curve and the AUC

The problem of interest is to estimate the concentration curve and the area under the curve (AUC) by estimating the parameters of a linear regression model with an autocorrelated error process. We introduce a simple linear unbiased estimator of the concentration curve and the AUC. We show that this estimator constructed from a sampling design generated by an appropriate density is asymptotically optimal in the sense that it has exactly the same asymptotic performance as the best linear unbiased estimator. Moreover, we prove that the optimal design is robust with respect to a minimax criterion. When repeated observations are available, this estimator is consistent and has an asymptotic normal distribution. Finally, a simulated annealing algorithm is applied to a pharmacokinetic model with correlated errors.